DeepAI
Log In Sign Up

The Intrinsic Manifolds of Radiological Images and their Role in Deep Learning

07/06/2022
by   Nicholas Konz, et al.
0

The manifold hypothesis is a core mechanism behind the success of deep learning, so understanding the intrinsic manifold structure of image data is central to studying how neural networks learn from the data. Intrinsic dataset manifolds and their relationship to learning difficulty have recently begun to be studied for the common domain of natural images, but little such research has been attempted for radiological images. We address this here. First, we compare the intrinsic manifold dimensionality of radiological and natural images. We also investigate the relationship between intrinsic dimensionality and generalization ability over a wide range of datasets. Our analysis shows that natural image datasets generally have a higher number of intrinsic dimensions than radiological images. However, the relationship between generalization ability and intrinsic dimensionality is much stronger for medical images, which could be explained as radiological images having intrinsic features that are more difficult to learn. These results give a more principled underpinning for the intuition that radiological images can be more challenging to apply deep learning to than natural image datasets common to machine learning research. We believe rather than directly applying models developed for natural images to the radiological imaging domain, more care should be taken to developing architectures and algorithms that are more tailored to the specific characteristics of this domain. The research shown in our paper, demonstrating these characteristics and the differences from natural images, is an important first step in this direction.

READ FULL TEXT
04/18/2021

The Intrinsic Dimension of Images and Its Impact on Learning

It is widely believed that natural image data exhibits low-dimensional s...
06/02/2019

Dimensionality compression and expansion in Deep Neural Networks

Datasets such as images, text, or movies are embedded in high-dimensiona...
07/06/2022

The Union of Manifolds Hypothesis and its Implications for Deep Generative Modelling

Deep learning has had tremendous success at learning low-dimensional rep...
06/01/2018

Intrinsic Isometric Manifold Learning with Application to Localization

Data living on manifolds commonly appear in many applications. We show t...
05/11/2020

On the Transferability of Winning Tickets in Non-Natural Image Datasets

We study the generalization properties of pruned neural networks that ar...
09/16/2013

Estimation of intrinsic volumes from digital grey-scale images

Local algorithms are common tools for estimating intrinsic volumes from ...
01/28/2022

DELAUNAY: a dataset of abstract art for psychophysical and machine learning research

Image datasets are commonly used in psychophysical experiments and in ma...

Code Repositories

radiologyintrinsicmanifolds

Code for reproducing results of the MICCAI 2022 paper "The Intrinsic Manifolds of Radiological Images and their Role in Deep Learning"


view repo